가끔 사이즈를 까먹어서 한번 데이터셋을 정리해보자!
1. MNIST
MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges
yann.lecun.com
train set 60000, test set 10000, 클래스 10개
사이즈: 28x28 grayscale, 32 bit integer
2. SVHN (The Street View House Numbers)
The Street View House Numbers (SVHN) Dataset
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits),
ufldl.stanford.edu
73257 digits for training, 26032 digits for testing, and 531131 additional
사이즈: 32*32 RGB
3. CIFAR
CIFAR-10 and CIFAR-100 datasets
< Back to Alex Krizhevsky's home page The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000
www.cs.toronto.edu
- CIFAR-10: train set 50000, test set 10000, 클래스 10개
- CIFAR-100: train set 50000, test set 10000, 클래스 100개 (한 클래스당 train 500, test 100)
사이즈: 32x32 RGB
4. ImageNet
ImageNet
Download ImageNet Data The most highly-used subset of ImageNet is the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012-2017 image classification and localization dataset. This dataset spans 1000 object classes and contains 1,281,167 training
image-net.org
1,281,167 training images, 50,000 validation images and 100,000 test images, 클래스 1000개
사이즈: 평균 469x387, RGB
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